A predictive model is based on historical data: it analyzes past data to predict the future.
In the Customer fit model, the segment of a lead (very good, good, medium, low) is defined by the model according to the result returned by historical conversions. However you may want to override the model to force the segment of some leads based on one or more traits for few reasons:
- You have not historically converted a lot of Enterprise companies but you are going up market and want to make sure Enterprise leads are routed to Sales
- You have converted some small companies in the past but you do not want to waste your Sales team skill on companies with less than 50 employees.
- There isn't clear evidence from your data which job roles and titles are the best but you want to boost some specific job titles based on your Sales team feedback.
For example, your company has not converted a lot of leads from Enterprise companies, therefore the model "naturally" scores them medium because your historical data say these leads don't convert well. However, you still want to prioritize these leads as very good for your Sales team to go after. To do so, you would apply an override like "If the company size of the lead is more than XXXX, then it should be scored very good" [regardless of what the historical data say].
How to add an override to a live model?
Step 1: Duplicate the live model
- Go to the Data Science Studio (https://springbok.madkudu.com)
- Duplicate the model marked as "live" so you don't risk editing the model currently in production
- Name the duplicated model how you want
Step 2: Create the override
- In the model, navigate to Data Science Studio > Overrides
- Click on Create new rule
- Select Form mode (preferred)
- Select Advanced mode if you need a complex logic not supported by the form node. You'll need to use SQL conditions like you would in a WHERE statement.
- Select the Computation, condition, value, rule and segment to create an override
- Check the box Case insensitive to make the override work regardless of upper/lower cases in the value.
- Click on Add sub-condition to add an AND condition
- If you need an OR condition, just create another override
- Click on Save rules
Step 3: Assess the impact of the override
You would want to make sure this override is not boosting or penalizing too many leads in a specific segment degrading the performance of the model.
To compare the impact of the override you just added on the training dataset
Head to the Check overrides performance tab to look at the performance on the training dataset. The difference in performance between the tab Ensembling and this tab is the impact of all the overrides on the performance.
To compare the impact of the override you just added on the validation dataset
Open the live model on your browser, head to the Validation tab, and compare the performance graph you see to the Validation tab of the model you added to override.
Step 4: Deploy override
- Coming soon for Architects! In the meantime, please send a request to email@example.com
Keep these considerations in mind when deploying overrides:
- All your Leads, Contacts, or Accounts usually scored by MadKudu will get rescored with the next batch scoring within the next 4-8hours.
- Adding, editing, or deleting overrides that increase or decrease prospect scores may trigger automated workflows in your CRM which are based on the customer fit or lead grade score (like your MQL workflow).
If a lead falls in an override "should be low" and another "should be very good", which override is applied?
Overrides penalizing the score of a lead or account has priority over an override boosting the score.
Ex: There is
- an override "IF country in Mexico, Then should be low"
- and an override "If employees > 10,000 AND industry = Internet Software & Services"
Then a 10k software company based in Mexico would be scored low, regardless of how the override are listed in the interface.
I don't see my CRM field in the picklist, what should I do?
The picklist contains Computations and not the list of your CRM fields. To create a computation from your CRM field, please follow the following steps
- Make sure your CRM field is pulled in the MadKudu platform
- Map your CRM field in the Attribute mapping which will make it available to the Data Science Studio
- Create a computation and release it
I created a computation but I don't see it in the picklist, what should I do?
Make sure you have released the computation, following Step 2 of this article.
If still unavailable, please reach out to firstname.lastname@example.org